جست‌وجوی استاد و پژوهشگر

Shanna Swanمشاهده پروفایل

استاد

• Environmental Health
• Public Health
• Reproductive Biology
• +۳ مورد دیگر

Shanna Swan is a renowned epidemiologist and Professor of Environmental Medicine and Public Health at the Icahn School of Medicine at Mount Sinai. She holds a PhD in Statistics from UC Berkeley (1963), an MA in Biostatistics from Columbia University, and a BA in Mathematics from City College of New York. Her career spans academia, public health institutions, and research on environmental health impacts. Notable roles include work at Kaiser Permanente, California Department of Health Services, University of Missouri, and University of Rochester. Her research focuses on endocrine-disrupting chemicals (EDCs), sperm count decline, and reproductive health. Her groundbreaking 2017 study revealed a 50% sperm count drop in Western men over 40 years, later updated to show acceleration since 2000. She authored the influential book Count Down (2021), addressing environmental threats to human fertility. Key contributions include forming California’s reproductive health group and leading National Academy of Sciences committees on EDCs. Swan advocates for science-driven public health policy, emphasizing the need to address chemical exposures. Her work bridges statistical rigor with real-world impact, influencing global discussions on fertility and environmental safety. Awards include the Ward Medal in Logic (CCNY). She remains active in advancing research, education, and community action to safeguard human health and reproduction.

Dmitri Krioukovمشاهده پروفایل

دانشیار

• Network Science
• Complex Networks
• Mathematical Physics
• +۷ مورد دیگر

Prof. Dmitri Krioukov is an Associate Professor in the Department of Physics at Northeastern University and holds an affiliated faculty position in Electrical and Computer Engineering. He directs the DK-Lab at the Network Science Institute, focusing on theoretical aspects of complex networks, including latent network geometry, random geometric graphs, and navigation in networks. His work bridges mathematical physics and applied network science, with applications to real-world data such as the Internet's structure. Research interests revolve around the interplay between network topology and geometry, including studies of causal sets, graph curvature, and dynamics in complex systems. He has pioneered frameworks linking network growth to hyperbolic geometry, enabling efficient routing algorithms. Notable contributions include the discovery of latent geometric structures underlying real-world networks and their implications for navigation and scalability. He has been recognized for high-impact publications, including multiple Stanford University Annual Assessments placing him among the top 2% most-cited scientists in his field (2024, 2023, 2022). His lab's interdisciplinary approach integrates principles from physics, mathematics, and computer science to address fundamental questions in network science.

• Statistics
• Data Science
• Applied Probability
• +۱ مورد دیگر

Alex Shkolnik is an Assistant Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara (UCSB). His email is shkolnik@pstat.ucsb.edu. While specific research interests, educational background, grants, or lab affiliations are not detailed in the provided text, his department affiliation suggests expertise in statistical methodologies and applied mathematical sciences. No awards, advised students, or publications are listed in the current information. Further details about his academic trajectory or professional activities would require additional sources.

Beata Csathoمشاهده پروفایل

استاد

• Remote Sensing
• Glaciology
• Climate Change
• +۳ مورد دیگر

Beata Csatho PhD is a Professor in the Department of Earth Sciences at the University at Buffalo, affiliated with the College of Arts and Sciences. Her research focuses on remote sensing, glaciology, climate change, and geophysics. She holds a PhD in Geophysics from the University of Miskolc, Hungary (1993). Her work integrates geophysical, remote sensing, and climatic data to study ice sheet dynamics and cryospheric changes. She leads the Remote Sensing lab and teaches courses like GLY 465/565 (Environmental Remote Sensing) and GLY 325 (Geophysics). Recent research emphasizes Greenland and Antarctic ice dynamics, ICESat-2 validation, and developing tools like Ghub for collaborative glaciology. She advises PhD and Master's students and collaborates on major projects like ISMIP7 and IceBridge. Her lab focuses on advancing laser altimetry, DEM correction, and cryosphere observation techniques.

Teddy Mekonnenمشاهده پروفایل

استادیار

• Information Economics
• Mechanism Design
• Industrial Organization
• +۳ مورد دیگر

Teddy Mekonnen is an Orlando Bravo Assistant Professor of Economics at Brown University's Department of Economics. Previously, he was a Linde Postdoctoral Fellow at Caltech (California Institute of Technology). He holds a PhD in Economics from Northwestern University (2017) and a BA in Economics with a Mathematics minor from Washington University in St. Louis (2011). His research focuses on information economics and mechanism design, particularly studying informational externalities, incentives for information acquisition/sharing, and their applications to industrial organization and political economy. He also explores decision theory and static/dynamic settings. Teaching responsibilities include courses such as ECON 1110 (Intermediate Microeconomics), ECON 2060 (Microeconomics II), and ECON 2970 (Workshop in Economic Theory). His recent research spans topics like search market efficiency, competition dynamics, and Bayesian comparative statics. Though no explicit grants or awards are listed, his work reflects deep engagement with theoretical and applied economic problems.

• Machine Learning
• Artificial Intelligence
• Explainability of AI
• +۳ مورد دیگر

Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University and serves as Director of the Stanford AI Lab (SAIL) and Senior Fellow at the Institute for Human-Centered AI (HAI). He holds dual roles as Chief Scientist at Visual Layer and Virtue AI. His research focuses on machine learning methods, explainability, fairness, and ethics of AI, alongside systems for scalable AI deployment. Education details are not explicitly provided, but his work spans foundational contributions to machine learning systems (e.g., XGBoost) and explainable AI frameworks like Anchors and LIME. He emphasizes ethical AI through projects like CheckList for model testing and Model Equality Testing for API transparency. His scientific contributions include advancing optimization techniques (AdaScale SGD, TVM compiler) and ethical benchmarks for generative AI. He has been recognized as a Member of the National Academy of Engineering for his transformative impact on AI systems and their societal applications. Guestrin leads interdisciplinary initiatives at SAIL and HAI, fostering collaboration between technical innovation and human-centered design. His work bridges theory and practice, addressing challenges in healthcare (diabetes management systems) and AI security.

John Wakeleyمشاهده پروفایل

استاد

• Theoretical Population Genetics
• Mathematical Biology
• Evolutionary Biology
• +۳ مورد دیگر

John Wakeley is a Professor of Organismic and Evolutionary Biology at Harvard University's Faculty of Arts and Sciences. He leads the Wakeley Lab, focusing on theoretical population genetics, mathematical models of genetic variation, and evolutionary processes. His research integrates analytical and computational methods to study contemporary and historical factors shaping genetic diversity. As of 2023, he is not accepting new graduate students for the academic year 2023-2024. Wakeley's work emphasizes coalescent theory, population structure, and evolutionary game theory. Notable contributions include developing statistical tools for analyzing ancient DNA and advancing models of ancestry reconstruction. Recent projects explore topics such as recurrent mutation in rare variants and the implications of big family effects on coalescence patterns. His lab members include researchers like Louis Fan, Jack Edwards, and Erin Ciccone, collaborating on diverse projects in theoretical and applied population genetics. Key scientific outputs include studies on iterated survival games and genomic analyses of butterfly radiation.

Bingzhang Chenمشاهده پروفایل

مدرس ارشد

• Biological oceanography
• Theoretical Ecology
• Ecosystem modeling
• +۹ مورد دیگر

Bingzhang Chen is a Senior Lecturer in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. He previously held positions as a Chancellor’s Fellow at the same institution, a researcher at the Japan Agency of Marine-Earth Science and Technology (JAMSTEC), and was affiliated with Xiamen University and Mount Allison University. His academic journey began with a PhD from the Hong Kong University of Science and Technology. Education: PhD in Trophic interactions within the microbial food web, Hong Kong University of Science and Technology (Awarded 2009) His primary research interests lie at the intersection of biological oceanography and theoretical ecology, with a strong focus on ecosystem modeling. He investigates how biodiversity, particularly of phytoplankton, influences marine ecosystem functioning such as primary production and the biological carbon pump. A central theme in his work is understanding the differential temperature sensitivity between autotrophs and heterotrophs, a question that bridges statistical analysis, metabolic theory, and Earth system science. His recent publications highlight a consistent trend in developing and applying individual-based models (e.g., PIBM 1.0), analyzing large datasets on plankton thermal responses, and studying the impacts of climate change and anthropogenic activities (like nutrient input) on marine microbial communities across diverse regions from the South China Sea to the North Pacific and Scottish coastal waters. His work often combines modeling with observational data to address fundamental ecological questions. Scientific Awards: David Cushing Prize (2015) from the Journal of Plankton Research New Century Excellent Talent (2012) from the Ministry of Education of China Dr. Chen is actively involved in research supervision, currently guiding five PhD students. He has been the Principal Investigator on multiple research projects funded by organizations such as the Leverhulme Trust, FILAMO, and the National Science Foundation. His expertise in programming (R, Fortran, MATLAB) underpins his methodological approach. He also contributes to the scientific community as an Associate Editor for the prestigious journal Limnology and Oceanography . His work is associated with efforts to understand and model invasive species dynamics, such as the spread of Sargassum muticum in Scottish waters, and he is involved with external advisory groups like the MASTS Marine Artificial Intelligence Forum.

Amanda Giangمشاهده پروفایل

استادیار

• Environmental Policy Analysis
• Air Quality Modeling
• Decarbonization Strategies
• +۴ مورد دیگر

Amanda Giang serves as Assistant Professor at the University of British Columbia's Faculty of Applied Science, Department of Mechanical Engineering, holding a Canada Research Chair in Environmental Modelling for Policy. She maintains a joint appointment with the Institute for Resources, Environment and Sustainability (IRES). Her educational background includes a B.A.Sc. from the University of Toronto, followed by M.S. and Ph.D. degrees from MIT, with postdoctoral training at MIT and Harvard. Dr. Giang's research employs interdisciplinary approaches to develop modeling tools for environmental policy analysis, focusing on pollution assessment, environmental injustice, and the intersection of air quality, decarbonization, and equity. Her work emphasizes action-oriented partnerships with community organizations and government health/environment agencies. Current projects address freight transport decarbonization equity, cumulative impact assessment methodologies for overburdened communities, and holistic environmental impact evaluation in technology design. Her recent publications demonstrate expertise across environmental modeling, policy analysis, and justice frameworks, with significant contributions to understanding spatial inequities in environmental risk distribution and developing community-engaged research methodologies. UBC Killam Research Prize, 2023 Dr. Giang actively collaborates with community groups and government authorities through her LEAP (Learning, Environmental Assessment, and Policy) research group. Her work integrates technical modeling with real-world policy applications, particularly in urban environmental planning contexts where equity considerations are paramount. She has developed innovative frameworks for cumulative impact assessment and environmental justice analysis that directly inform regulatory decision-making processes. Her research laboratory focuses on developing open-source modeling tools for environmental policy analysis while maintaining strong community partnerships that ensure research addresses pressing local environmental justice concerns.

Yian Maمشاهده پروفایل

استادیار

• Scalable Inference Methods
• Time Series Data
• Sequential Decision Making
• +۴ مورد دیگر

Yian Ma is an Assistant Professor at the University of California San Diego (UCSD). His research focuses on scalable inference methods, time series analysis, and sequential decision making, with an emphasis on developing Bayesian algorithms for uncertainty quantification and establishing their computational-statistical guarantees. Prior to UCSD, he served as a post-doctoral fellow at UC Berkeley. He holds a Ph.D. from the University of Washington and a bachelor’s degree from Shanghai Jiao Tong University. Education: Ph.D., University of Washington Bachelor's, Shanghai Jiao Tong University Research Interests: Yian Ma’s work bridges theoretical foundations and practical scalability in machine learning. He explores advanced Bayesian methodologies to address challenges in dynamic data analysis and decision processes, ensuring rigorous guarantees for both computational efficiency and statistical accuracy.

Magnus Bakke Botnanمشاهده پروفایل

استادیار

• Topological Data Analysis
• Representation Theory of Quivers
• Computational Topology
• +۳ مورد دیگر

Magnus Bakke Botnan is an Assistant Professor at the Department of Mathematics, Vrije Universiteit Amsterdam, holding a VIDI career grant (€850,000) since 2018. His research bridges pure and applied mathematics within topological data analysis (TDA), focusing on multiparameter persistence, computational topology, and applications to sciences. PhD in Mathematics, Norwegian University of Science and Technology (NTNU), 2015 Postdoc at TU Munich, 2016-2018 His research group includes postdocs Hannah Rocio Santa Cruz Baur and Rui Dong, and PhD student Enes Devecioğlu. Recent work involves signed barcodes, rank decompositions, and stability of persistence modules. He co-authored the first comprehensive tutorial on multiparameter persistence with Mike Lesnick. Notable contributions include proving the NP-hardness of computing interleaving distance, establishing universality of bottleneck distance for extended persistence diagrams, and developing computational methods for non-branching complexes. Publications span journals like Foundations of Computational Mathematics , Discrete & Computational Geometry , and conferences SoCG, NeurIPS, and ICRA. Scientific Awards: VIDI Career Grant (€850,000) He has taught courses including Complex Analysis, Calculus, Topological Data Analysis, and seminars on analysis and dynamical systems. Actively organizes Applied Topology Days and collaborates on projects integrating TDA with physics, computer science, and statistics.

Bernd Luckeمشاهده پروفایل

استاد

• Macroeconomics
• Monetary Policy
• European Economic Policy
• +۷ مورد دیگر

Prof. Dr. Bernd Lucke is a full professor at the University of Hamburg , affiliated with the Faculty of Business, Economics and Social Sciences and Department of Economics . He holds the Chair for Economic Growth and Business Cycles and has been active in research, teaching, and public policy discourse. Research Interests : Macroeconomics, Monetary Policy, European Economic Policy, Business Cycles, Econometrics, Development Policy, and Public Finance. Publications : His recent work (2025–2020) focuses on synthetic control methods for EU monetary analysis, ECB policy critiques, debt sustainability, digital currency legislation, and expropriation impacts on FDI. Earlier works span econometric testing of Ricardian equivalence, productivity shocks, and growth modeling. Media Contributions : Regular political commentary in outlets like Cicero and Frankfurter Allgemeine Zeitung , often critiquing ECB decisions, EU fiscal integration, and inflation dynamics. Contact : bernd.lucke@uni-hamburg.de

• Latent Variable Modeling
• Machine Learning
• Measurement Equivalence
• +۳ مورد دیگر

Dr. David Goretzko is an Assistant Professor in the Department of Methodology and Statistics at Utrecht University's Faculty of Social and Behavioural Sciences. He leads the Measurement and Machine Learning Lab and specializes in the integration of data science techniques with psychometric theory. His academic journey includes: Ph.D. in Psychological Methods from LMU Munich (2020) M.Sc. in Statistics from LMU Munich (2018) M.Sc. in Psychology from LMU Munich (2016) B.Sc. in Physics from LMU Munich (2015) B.Sc. in Psychology from LMU Munich (2014) Dr. Goretzko's research primarily focuses on the intersection of machine learning and psychometrics. His work addresses critical challenges in factor analysis, measurement invariance, and model fit assessment. He develops innovative methods that combine traditional psychometric approaches with modern data science techniques, particularly in the areas of exploratory factor analysis trees, regularized factor analysis, and cost-sensitive machine learning applications in psychological assessment. His research has significant implications for improving the validity and reliability of psychological measurements across diverse populations. His recent publications reveal a strong trend toward integrating machine learning methodologies with traditional psychometric approaches. A significant portion of his work focuses on factor analysis techniques, particularly addressing the challenge of determining the appropriate number of factors. His research also extensively covers measurement invariance testing across multiple covariates using tree-based approaches, and he has made notable contributions to evaluating model fit in confirmatory factor analysis. The interdisciplinary nature of his work spans psychology, statistics, and computer science. Dr. Goretzko serves as an Associate Editor for the European Journal of Psychological Assessment and is an active reviewer for numerous prestigious journals including Psychological Methods, Behavior Research Methods, and Structural Equation Modeling. He also reviews grant proposals for major funding agencies such as the German Research Foundation (DFG), National Science Foundation (NSF), and Dutch Research Council (NWO). He currently holds a Project Grant from the German Research Foundation (DFG GO 3499/1-1) since 2021. His research program focuses on developing and validating new methodologies for psychological assessment that incorporate machine learning techniques while maintaining psychometric rigor. His work has practical applications in educational measurement, clinical psychology, and organizational assessment. Dr. Goretzko leads the Measurement and Machine Learning Lab at Utrecht University, which focuses on developing innovative methodologies that bridge the gap between traditional psychometrics and modern data science. The lab's research has particular relevance for improving measurement practices in cross-cultural research, educational assessment, and clinical psychology settings where measurement invariance and factor structure validation are critical concerns.

Petter N. Kolmمشاهده پروفایل

استاد بالینی

• Quantitative Trading Strategies
• Delegated Portfolio Management
• Financial Econometrics
• +۵ مورد دیگر

Petter N. Kolm serves as a Clinical Professor of Mathematics and Program Director at New York University, with his office located in Warren Weaver Hall (520). He can be contacted at petter.kolm@nyu.edu or 212-998-4855, and holds an editorial board position at the Journal of Portfolio Management. His academic qualifications include: Doctorate in Mathematics from Yale University M.Phil. in Applied Mathematics from the Royal Institute of Technology in Stockholm M.S. in Mathematics from ETH Zurich Dr. Kolm's research centers on quantitative finance, with primary focus areas including quantitative trading strategies, delegated portfolio management, financial econometrics, risk management, and optimal portfolio strategies. His work integrates advanced mathematical modeling with practical investment applications, bridging theoretical frameworks and real-world market dynamics through rigorous empirical analysis. Analysis of his 15 most recent publications reveals consistent emphasis on portfolio optimization techniques—particularly Bayesian methods and the Black-Litterman model—alongside significant contributions to algorithmic trading systems, factor-based equity portfolio construction, and machine learning applications for financial sentiment analysis. His scholarly output demonstrates evolution from foundational portfolio theory toward contemporary computational finance challenges. As Program Director, Dr. Kolm oversees academic programming and likely mentors graduate students in quantitative finance, though specific advisee details are not documented. His prior industry role at Goldman Sachs Asset Management provided direct experience in developing hedge fund strategies, informing his applied research approach. Dr. Kolm's professional trajectory includes significant industry engagement through his tenure in Goldman Sachs' Quantitative Strategies Group, where he developed quantitative investment systems. His current academic leadership position leverages this practical experience to shape quantitative finance education and research at NYU.

Carlos Fernandez-Grandaمشاهده پروفایل

دانشیار

• Machine Learning
• Medical AI
• Climate Science
• +۷ مورد دیگر

Carlos Fernandez-Granda is an Associate Professor of Mathematics and Data Science at New York University, holding joint appointments at the Courant Institute of Mathematical Science and the Center for Data Science. He currently serves as the Interim Director of the Center for Data Science. His academic career spans over a decade at NYU, where he has taught probability and statistics to data-science students. Dr. Fernandez-Granda's research focuses on designing and analyzing data-science methodology, with current emphasis on machine learning applications in medicine, climate science, and scientific imaging. His work bridges theoretical foundations with practical applications across multiple domains. His notable contributions include: Development of the COBRA (COnfidence-Based chaRacterization of Anomalies) score for automatic assessment of impairment and disease severity Applications of machine learning to improve climate projections through the M2LInES project Research on magnetic resonance fingerprinting for quantitative tissue parameter estimation Development of AI systems for medical diagnostics including Alzheimer's detection and breast cancer diagnosis Dr. Fernandez-Granda is the author of the book "Probability and Statistics for Data Science," published by Cambridge University Press. The book serves as a comprehensive guide to the two pillars of data science, featuring real-world datasets and addressing fundamental challenges like overfitting, the curse of dimensionality, and causal inference. His research has been supported by grants from the National Science Foundation (Division of Mathematical Sciences, grants 1616340 and 2009752) and the Alzheimer's Association (grant AARG-NTF-21-848627). Dr. Fernandez-Granda is actively involved in several collaborative projects: M2LInES project: An international collaboration focused on improving climate projections using machine learning to capture unaccounted physical processes at the air-sea-ice interface Math and Data group: Exploring the intersection of mathematical theory and data science applications